Abstract 4080: New platform for novel biomarker discovery using single-cell RNA sequencing on pediatric acute myeloid leukemia samples
Authors: Robert Schauner, Zachary Jackson, Nethrie Idippily, Grace Lee, Sheela Karunanithi, Shivaprasaad Manjappa, Tae Hyun Hwang, David Wald
Journal: Cancer Research (2022)

Abstract Pediatric Acute Myeloid Leukemia (AML) with a FLT3 internal tandem duplication (FLT3-ITD) is a challenging disease due to poor outcomes in many patients. The 4-year progression-free survival is still only 31%. Current biomarkers are insufficient to predict why certain patients with FLT3-ITD AML relapse and others do not. The development of prognostic biomarkers in FLT3-ITD pediatric AML may help improve the outcomes and management of these patients. We acquired a panel of 37 diagnostic samples with 18 samples from FLT3-ITD pediatric AML patients with poor outcomes (relapse within 3 years) and 19 from favorable outcome patients that did not exhibit relapse for at least 5 years. Next, we performed FACS to isolate CD34+CD38dim and CD34+CD38+ cells. These two populations were then sequenced using 10x Genomics 3’ end single-cell RNA sequencing (scRNAseq). Using this scRNAseq dataset comprising over 250k single cells, we first investigated if the frequency of specific RNA clusters of AML cells may predict patient outcome. We found 5 clusters had significantly different frequencies between patients with favorable and poor outcomes (p &amp;lt; 0.05). Using these clusters, we performed CIBERSORTx deconvolution on publicly available FLT3-ITD AML bulk RNAseq samples from TCGA (n = 36), BeatAML (n = 47), and the TARGET project (ntrain = 72; ntest = 26) to identify the inferred cluster composition present in each AML patient from these datasets. Next we built LASSO prediction models using the inferred scRNAseq clusters by CIBERSORTx and incorporating clinical features including leukemic blast percentage, bone marrow leukemic blast percentage, and WBC at diagnosis. We found these prediction models to be highly specific as a prognostic biomarker for pediatric AML (p &amp;lt;&amp;lt; 0.01). We were unable to train a model directly using genes from the publicly available bulk RNAseq data for pediatric FLT3-ITD AML, highlighting the relevance of using our deconvolution based biomarker. Between the high (&amp;gt; median) and low (&amp;lt; median) LASSO score groups, there was ~50% difference in the number of patients relapsing after 4 years. When we included this LASSO score in a multivariate cox regression (likelihood ratio p value = 0.007), the LASSO score (HR = 6.8, p = 0.026, CI = 1.26 - 37.28) showed additional predictive ability when including FLT3-ITD allelic ratio (HR = 1.83, p = 0.38, CI = 0.47 - 7.15) and MRD (HR = 3.95, p = 0.021 CI = 0.051 - 0.79). These results demonstrate that incorporating rare AML cell populations into a prognostic biomarker could add substantial clinical information to existing biomarkers. This indicates single-cell derived prognostic models can and should be explored further for pediatric FLT3-ITD AML and demonstrates a platform that could be used for other malignancies, including non-hematologic tumors, to create cell-type biomarkers. Citation Format: Robert Schauner, Zachary Jackson, Nethrie Idippily, Grace Lee, Sheela Karunanithi, Shivaprasaad Manjappa, Tae Hyun Hwang, David Wald. New platform for novel biomarker discovery using single-cell RNA sequencing on pediatric acute myeloid leukemia samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 4080.